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Record W2792357137 · doi:10.5539/ijel.v8n4p83

Retribution in Biblical Texts: A Stylistic Analysis

2018· article· en· W2792357137 on OpenAlexvenueno aff
Riyadh Tariq Kadhim Al-Ameedi, Saja Abdul Ameer Al-A’ssam

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsnot available
Fundersnot available
KeywordsRetributive justiceBiblical studiesCharacter (mathematics)Function (biology)LinguisticsLiteratureInterpretation (philosophy)Computer sciencePhilosophyArtEconomic JusticeMathematicsLaw

Abstract

fetched live from OpenAlex

Divine retribution is both a teaching and a core-tenet in Christianity. A large portion of Biblical texts come to embody the righteous judgment of the Almighty Lord, His gracious as well as wrath character. This paper is an attempt to pinpoint the stylistic devices and features of retribution in Biblical texts. It aims to identify the stylistic phonological, syntactic, and semantic devices of retribution in Biblical texts and find out the function of each. Furthermore, it aims to explain the overall functions such texts of retribution perform and how the overall function of these texts and the functions of the utilized stylistic devices are coalesced to produce a stylistic interpretation to these texts. It is hypothesized that Biblical texts of retribution utilize specific stylistic phonological, syntactic, and semantic devices. Besides, it is hypothesized that the functions these devices perform fall in line with the overall functions the Biblical texts of retribution have. A model is developed to analyze the Biblical texts of retribution. In addition, statistical findings are used to support the results. The findings of the analysis validate the hypotheses mentioned above.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.294
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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